Track accepted paper

CiteScore:
5.24ℹCiteScore:2018: 5.240CiteScore measures the average citations received per document published in this title. CiteScore values are based on citation counts in a given year (e.g. 2015) to documents published in three previous calendar years (e.g. 2012 – 14), divided by the number of documents in these three previous years (e.g. 2012 – 14).

Impact Factor:
4.674ℹImpact Factor:2018: 4.674The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years.
2018 Journal Citation Reports (Clarivate Analytics, 2019)

5-Year Impact Factor:
4.807ℹFive-Year Impact Factor:2018: 4.807To calculate the five year Impact Factor, citations are counted in 2018 to the previous five years and divided by the source items published in the previous five years.
2018 Journal Citation Reports (Clarivate Analytics, 2019)

Source Normalized Impact per Paper (SNIP):
1.622ℹSource Normalized Impact per Paper (SNIP):2018: 1.622SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.

SCImago Journal Rank (SJR):
1.593ℹSCImago Journal Rank (SJR):2018: 1.593SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal’s impact.

Author StatsℹAuthor Stats:Publishing your article with us has many benefits, such as having access to a personal dashboard: citation and usage data on your publications in one place. This free service is available to anyone who has published and whose publication is in Scopus.

As we approach the end of the second decade of the 21st century, we may envisage a completely different paradigm for generating knowledge, which might become a reality in a few decades. According to this new paradigm, artificial systems (machines!) will be able to generate knowledge; that is to say, for the first time in history, knowledge would be created without human intervention. This prediction is based upon developments in machine learning following decades of intense research, which have achieved innovative leaps in recent years. Most notably, relevant progress in prediction schemes, classification methods, and advanced modelling have made it possible for machines to outperform humans in various intellectually-demanding tasks.

Very stringent requirements must be met before this paradigm becomes a reality, of which perhaps the most important ones are associated with challenges in the so-called Big Data methodologies. The first step towards having autonomous machines capable of generating knowledge is the fostering of data-intensive discovery. In fact, the challenges in storing, managing, sharing and mining massive amounts of data are far from trivial. Several layers of resources and tools are required, which include enormous storage, data security, information management, and, overall, efficacious machine intelligence.

These new concepts can obviously be applied to any field, and materials science is no exception. Any technology based upon complex devices or hardware relies on materials science. With the large body of data detected by a diversity of computerized sensors during experimental testing, it is essential that automatic tools be designed so that Big Data translates into useful knowledge. Many efforts are being made around the world; therefore, it is time to analyse what has been accomplished in a collection of selected papers dealing with machine learning applied to materials science.

In this article selection, the reader will find compelling examples from different areas of materials science. Just to give a flavour of what has been compiled, we mention the diversity of materials and applications in a sample of interesting papers. From the development of concrete for civil construction, as presented in the paper Machine learning in concrete strength simulations: Multi-nation data analytics, by Chou et al., to the design of lithium-ion batteries, as explained in Application of machine learning methods for the prediction of crystal system of cathode materials in lithium-ion batteries, by Shandiz and Gauvin. Common to these papers is evidence that machine learning does enhance human capability in predicting the properties of materials. This is also beautifully illustrated in the proposal Material synthesis and design from first principle calculations and machine learning, by Takahashi and Tanaka, who use a database of materials and their properties to “teach” machine learning methods to predict new materials with desirable traits; a similar approach is adopted by Khan, Shamsi and Choi in the contribution entitled Correlating dynamical mechanical properties with temperature and clay composition of polymer-clay nanocomposites, which relies on support vectors and artificial neural networks to identify non-linear correlations between temperature and composition and mechanical properties in polymer-clay nanocomposites.

The achievements highlighted in this selection include work heavily- focused on materials; as observed in power generation, a topic touched upon in Multi-Model Ensemble for day ahead prediction of photovoltaic power generation, by Pierro et al.; or in the case of medicine, as described in the paper Human breath-print identification by E-nose, using information-theoretic feature selection prior to classification, by Wang et al., who demonstrate how the breath of a patient has the potential to enable the detection of clinical conditions based on machine learning classification. Two papers demonstrate that computer vision might be improved by knowledge coming from machinery, as in the work A computer vision approach for automated analysis and classification of microstructural image data, by DeCost and Holm; and in the contribution Image driven machine learning methods for microstructure recognition, by Chowdhury et al. In these two later papers, the task of identifying the types of microstructures is performed by employing numeric visual features used to feed machine-driven methods.

We trust the novel ideas, concepts and results compiled in this issue will inspire further investigation on how to enable machines to “see” and infer beyond the ability of humans, which is highly constrained by limited perception, memory and reasoning. Such investigations are essential to pave the way towards better and more affordable solutions to critical problems affecting the quality of everyday human life.